Abstract
Understanding the various factors how the infections among the humans are provoked when any disease affects the microbiome of the human body which then leads to certain alterations. Microbiome studies are drawing an expanding interest, particularly in person’s wellbeing approach, where their usage in illness anticipating, indication and treatments can affect quality of life. In our investigation, we created the operational taxonomic unit (OTU) which contains moving pictures of microbiome of humans. The consequences obtained from OUT table encouraged us to select Random Forest (RF) classifier and Spectral grouping (SC). The Random Forest Classifier has performed well in terms of accuracy and precision. After the analysis we have observed that the alterations in the microbiome. This leads to development of certain module of invention or model of technology for detecting different diseases and to take preventing measure.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
O. Manor, R. Levy, E. Borenstein, Mapping the inner working of the microbiome: genomic—and metagenomic-based study of tMetabolism and metabolic interactions in the human microbiome. Cell Metab. 20(5), 742–752 (2014)
J.M. Janda, S.L. Abbott, 16s rrna gene sequencing for bacterial identification in the diagnostic laboratory: pluses, perils, and pitfalls. J. Clinical Microbiol. 45(9), 2761–2764 (2007) Online
M.L. Zepeda Mendoza, T. Sicheritz-Pontén, M.T.P.Gilbert, Environmental genes and genomes: understanding the differences and challenges in the approaches and software for their analyses. Brief. Bioinfo. 16(5), 745–758 02 (2015)
E. Aylagas, A. Borja, X. Irigoien, N. Rodríguez-Ezpeleta, Benchmarking dna metabarcoding for biodiversity-based monitoring and assessment. Front. Mar. Sci. 3, 96 (2016)
J. Kuczynski, C.L. Lauber, W.A. Walters, L.W. Parfrey, J.C. Clemente, D. Gevers, R. Knight,Experimental and analytical tools for studying the human microbiome (2011)
D. Gevers, R. Knight, J. F. Petrosino, K. Huang, A. L.McGuire, B.W. Birren, K.E. Nelson, O. White, B.A. Methé, C. Huttenhower, The human microbiome project: a community resource for the healthy human microbiome. PLoS Biol. 10(8), e1 001 377–e1 001 377 (2012), 22904687[pmid]
L.R. Thompson et al., A communal catalogue reveals earth’s multiscale microbial diversity. Nature 551, 457EP (2017) article
E.C. Dinleyici, D. Martínez-Martínez, A. Kara, A. Karbuz, N. Dalgic, O. Metin, A.S. Yazar, S. Guven, Z. Kurugol, O. Turel, M. Kucukkoc, O. Yasa, M. Eren, M. Ozen, J.M. Martí, C.P. Garay, Y. Vandenplas, A. Moya, Time series analysis of the microbiota of children suffering from acute infectious diarrhea and their recovery after treatment. Front. Microbiol. 9, 1230 (2018) [Online
B.J. Ridenhour, S.L. Brooker, J.E. Williams, J.T. Van Leuven, A.W. Miller, M.D. Dearing, C.H. Remien, Modeling time-series data from microbial communities. The ISME J 11(11), 2526–2537 (2017). 28786973[pmid] Online
S. Brdar, V. Crnojevic, Ensemble approaches for stable assessment of clusters in microbiome samples. in Computational Intelligence Methods for Bioinformatics and Bio-statistics. ed. by A. Bracciali, G. Caravagna, D. Gilbert, R. Tagliaferri CIBB 2016. Lecture Notes in Computer Science, vol. 10477 (Springer, Cham, 2017), pp. 199–208
Greengenes 16s rrna gene database. https://greengenes.secondgenome.com/, Access 11 July 2019
P. Yang, X. Su, L. Ou-Yang, H.-N. Chua, X.-L. Li, K. Ning, Microbial community pattern detection in human body habitats via ensemble clustering framework. BMC Syst. Biol. 8(4), S7 (2014)
Silva, High quality ribosomal rna database. https://www.arb-silva.de/. Accessed 11 July 2019
R.C. Edgar, Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26(19), 2460–2461 (2010)
N. Mantel, The detection of disease clustering and a generalized regression approach. Cancer Res. 27(2) Part 1, 209–220 (1967)
A.Y. Ng, M.I. Jordan, Y. Weiss, On spectral clustering: analysis and an algorithm. in Proceedings of the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic, ser. NIPS’01. (MIT Press, Cambridge, MA, USA, 2001), pp. 849–856 (2001)
L. Hubert, P. Arabie, Comparing partitions. J. Classif. 2(1), 193–218 (1985) [Online]
W.M. Rand, Objective criteria for the evaluation of clustering methods. J. Am. Stat. Assoc. 66(336), 846–850 (1971)
T.M. Qiime, Quantitative insights into microbial ecology. https://qiime.org/. Accessed 10 July 2019
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Vijan, H., Kharote, P.R. (2021). Identifying the Alterations in the Microbiome Using Classification and Clustering Analysis: A Path Towards Microbiome Bio-Tech Innovations. In: Kaiser, M.S., Xie, J., Rathore, V.S. (eds) Information and Communication Technology for Competitive Strategies (ICTCS 2020). Lecture Notes in Networks and Systems, vol 190. Springer, Singapore. https://doi.org/10.1007/978-981-16-0882-7_90
Download citation
DOI: https://doi.org/10.1007/978-981-16-0882-7_90
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-0881-0
Online ISBN: 978-981-16-0882-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)